Evidence map›Paper›PMID 29375343›Full record

ArticleFrontiers in human neuroscience2017

A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study.

Fatma E A El-Gamal, Mohammed M Elmogy, Mohammed Ghazal, Ahmed Atwan, Manuel F Casanova, Gregory N Barnes, Robert Keynton, Ayman S El-Baz, Ashraf Khalil

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fatma E A El-GamalFaculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt.
Mohammed M ElmogyFaculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt.
Mohammed GhazalBioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States.
Ahmed AtwanFaculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt.
Manuel F CasanovaSchool of Medicine, University of South Carolina, Greenville, SC, United States.
Gregory N BarnesUniversity of Louisville Autism Center, Department of Neurology, University of Louisville, Louisville, KY, United States.
Robert KeyntonDepartment of Bioengineering, University of Louisville, Louisville, KY, United States.
Ayman S El-BazBioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States.
Ashraf KhalilDepartment of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60-70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample

Indexed as

ADCADpersonalized diagnosisPiB-PETstatistical analysis

Identifiers

PMID29375343
PMCPMC5767309

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.